// @(#)root/tmva $Id: GiniIndex.cxx,v 1.3 2006/05/23 19:35:06 brun Exp $
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss

/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : TMVA::GiniIndex                                                       *
 *                                                                                *
 * Description: Implementation of the GiniIndex as separation criterion           *
 *              Large Gini Indices (maximum 0.5) mean , that the sample is well   *
 *              mixed (same amount of signal and bkg)                             *
 *              bkg. Small Indices mean, well separated.                          *
 *              general defniniton:                                               *
 *              Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2    *
 *              Where: M is a smaple of whatever N elements (events)              *
 *                     that belong to K different classes                         *
 *                     c(k) is the number of elements that belong to class k      *
 *              for just Signal and Background classes this boils down to:        *
 *              Gini(Sample) = 2s*b/(s+b)^2                                       *
 *                                                                                *
 * Description: Implementation of the GiniIndex as separation criterion           *
 *                                                                                *
 *                                                                                *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Xavier Prudent  <prudent@lapp.in2p3.fr>  - LAPP, France                   *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-KP Heidelberg, Germany     *
 *      Kai Voss        <Kai.Voss@cern.ch>       - U. of Victoria, Canada         *
 *                                                                                *
 * Copyright (c) 2005:                                                            *
 *      CERN, Switzerland,                                                        *
 *      U. of Victoria, Canada,                                                   *
 *      Heidelberg U., Germany,                                                   *
 *      LAPP, Annecy, France                                                      *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (http://mva.sourceforge.net/license.txt)                                       *
 *                                                                                *
 **********************************************************************************/

//_______________________________________________________________________
//
// Implementation of the GiniIndex as separation criterion
//
//_______________________________________________________________________

#include "TMVA/GiniIndex.h"

ClassImp(TMVA::GiniIndex)

//_______________________________________________________________________
Double_t TMVA::GiniIndex::GetSeparationIndex( const Double_t &s, const Double_t &b )
{
   //     Gini(Sample M) = 1 - (c(1)/N)^2 - (c(2)/N)^2 .... - (c(k)/N)^2
   //     Where: M is a smaple of whatever N elements (events)
   //            that belong to K different classes
   //            c(k) is the number of elements that belong to class k
   //     for just Signal and Background classes this boils down to:
   //     Gini(Sample) = 2s*b/(s+b)^2    ( = 2 * purity * (1-purity) )

   if (s+b <= 0) return 0.;
   if (s<=0 || b <=0) return 0.;
   //  else return 2 * s*b/(s+b)/(s+b);
   else return s*b/(s+b)/(s+b);      //actually the "2" should not influence the result
}